RCT-AD: Reliability-Aware Context and Temporal BEV Reasoning for End-to-End Autonomous Driving
RCT-AD: Reliability-Aware Context and Temporal BEV Reasoning for End-to-End Autonomous Driving
This research introduces RCT-AD, a framework designed to enhance end-to-end autonomous driving by explicitly modeling feature reliability and temporal consistency within a shared Bird's-Eye-View (BEV) representation.
The paper proposes RCT-AD, a framework for end-to-end autonomous driving that integrates reliability-aware context and temporal reasoning within a Bird's-Eye-View (BEV) representation to mitigate degradation from occlusion, motion blur, and sensor noise.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
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- Reported autonomousOpen source S1
Reported numbers
NDS score
61.5
View original evidence
diction, and trajectory planning. RCT-AD achieves 61.5 NDS, 52.9 mAP, and 52.3 mIoU while maintaining competitive computational efficiency. These results demonstrate the effectiveness of reliability-aware temporal BEV reasoning for unified perception, prediction, and planning.
Open source S6mAP score
52.9
View original evidence
diction, and trajectory planning. RCT-AD achieves 61.5 NDS, 52.9 mAP, and 52.3 mIoU while maintaining competitive computational efficiency. These results demonstrate the effectiveness of reliability-aware temporal BEV reasoning for unified perception, prediction, and planning.
Open source S6mIoU score
52.3
View original evidence
diction, and trajectory planning. RCT-AD achieves 61.5 NDS, 52.9 mAP, and 52.3 mIoU while maintaining competitive computational efficiency. These results demonstrate the effectiveness of reliability-aware temporal BEV reasoning for unified perception, prediction, and planning.
Open source S6
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
RCT-AD: Reliability-Aware Context and Temporal BEV Reasoning for End-to-End Autonomous Driving
Open source S1
diction, and trajectory planning. RCT-AD achieves 61.5 NDS, 52.9 mAP, and 52.3 mIoU while maintaining competitive computational efficiency. These results demonstrate the effectiveness of reliability-aware temporal BEV reasoning for unified perception, prediction, and planning.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org